In a vehicular communications environment, the need for information sharing, entertainment, and multimedia will increase, leading to congestion of backhaul networks. The major challenge of this network is latency and resource limitations. Proactive caching can be obtained from local caches rather than from remote servers, which can avoid long delays resulting from limited backhaul capacity and resources. Therefore, proactive caching reduces latency and improves the quality of services. Determining which files should be cached in memory is a critical issue. The paper proposes various placement

Planar Parallel manipulators (PPMs) are widely used these days, as they have many advantages compared to their serial counterparts. However, their inverse and direct kinematics are hard to obtain, due to the complexity of the manipulators' behavior. Therefore, this paper provides a comparative analysis for two methods that were used to obtain the inverse kinematics of a 3-RRR manipulator. Instead of the conventional algebraic and graphical methods used for attaining the mathematical models for such manipulators, an adaptive neuro-fuzzy inference structure (AFNIS) model was alternatively

This article proposes an intelligent approach to the Direct Power Control technique of the PWM rectifier, this control technique improves the performance of PWM converter, called Direct Power Control Based on Artificial Neural Network (ANN), applied for the selection of the optimal control vector. DPC-ANN ensures smooth control of active and reactive power in all Sectors and reduces current ripple. Finally, the developed DPC was tested by simulation, the simulation results proved the excellent performance of the proposed DPC scheme. © 2018 IEEE.

This article proposes a new problem which is called the Stochastic Travelling Advisor Problem (STAP) in network optimization, and it is defined for an advisory group who wants to choose a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. A nonlinear binary mathematical model is formulated and a real application case study in the occupational health and safety field is presented. The problem has a stochastic nature in travelling and advising times since the deterministic models are not appropriate for such real-life problems. The

This paper introduces a heuristic solver based on neural networks and deep learning for the knapsack problem. The solver is inspired by mechanisms and strategies used by both algorithmic solvers and humans. The neural model of the solver is based on introducing several biases in the architecture. We introduce a stored memory of vectors that holds up items representations and their relationship to the capacity of the knapsack and a module that allows the solver to access all the previous outputs it generated. The solver is trained and tested on synthetic datasets that represent a variety of

Sustainability is an important consideration in product design. The sustainable design should fully consider the environmental, social, and economic factors of the product. However, the three factors are often conflicting with each other. This paper aims to strike a balance between these factors and achieve sustainable product design through multi-objective optimization. The three influencing factors of sustainability, namely, the environmental factor, social factor and economic factor, were respectively defined as environmental impact, labor time and labor cost. Then, the product to be

Titanium dioxide (TiO2) nanocomposites have been extensively employed in many fundamental optoelectronic and photocatalytic applications due to their outstanding optical, electronic and chemical properties. In the present work, we introduce a simple layer-by-layer approach to design a magnetic TiO2 nanocomposite that could be easily recycled using an external magnetic field without affecting its quantum efficiency. The crystallinity, size, surface area, stability, morphology, purity and other optical, thermal and magnetic properties of the composite have been investigated. Surface topology

“There’s Plenty of Room at the Bottom: An Invitation to Enter a New Field of Physics” said Richard Feynman in 1959, this lecture opened the way to the new field of science which we know today as nanotechnology. Materials’ manipulation at a very small size, ranges from 1 to 100 nm (nanoworld or the nano-edge) is well-known as nanotechnology. Since then, a lot of investigations and research were devoted by many researchers around the globe to keep an eye on the different properties and behavior of nanomaterials. Materials with at least one nanoscale dimension are called nanomaterials that have

We report on the design and structure principles of 5,5′-6,6′-tetrachloro-1,1′-dioctyl-3,3′-bis-(3-carboxypropyl)-benzimidacarbocyanine (Dye 1). Such metal-free amphiphilic cyanine dyes have many applications in dye-sensitized solar cells. AFM surface topographic investigation of amphiphilic molecules of Dye 1 adsorbed on TiO2 anode reveals the ability of spontaneous self-organization into highly ordered aggregates of fiber-like structure. These aggregates are known to exhibit outstanding optical properties of J-aggregates, namely, efficient exciton coupling and fast exciton energy migration

A new strategy regarding the fabrication of chitosan (CS) or ethylene diamine tetraacetic acid (EDTA) on graphene oxide (GO) was performed. The nematocidal potential against Meloidogyne incognita causing root-knot infection in eggplant was tested. The plant immune response was investigated through measuring the photosynthetic pigments, phenols and proline contents, oxidative stress, and antioxidant enzymes activity. Results indicating that, the treatment by pure GO recorded the most mortality percentages of M. incognita 2nd juveniles followed by GO-CS then GO-EDTA. In vivo greenhouse